Background: In spite of considerable advancements in our understanding of the different factors involved in achieving vocabulary-learning success, the overall pattern and interrelationships of critical factors involved in L2 vocabulary learning - particularly, the mechanisms through which learners regulate their motivation and learning strategies - remain unclear.
Aims: This study examined L2 vocabulary learning, focusing on the joint influence of different motivational factors and learning strategies on the vocabulary breadth of adolescent learners of English as a foreign language (EFL) in China.
Sample: The participants were 107 tenth graders (68 females, 39 males) in China.
Methods: The data were collected via two questionnaires, one assessing students' motivation towards English-vocabulary learning and the other their English vocabulary-learning strategies, along with a test measuring vocabulary breadth.
Results: Structural equation modelling (SEM) indicated that learning strategy partially mediated the relationship between motivation (i.e., a composite score of intrinsic and extrinsic motivation) and vocabulary learning. Separate SEM analyses for intrinsic (IM) and extrinsic motivation (EM) revealed that there were significant and positive direct and indirect effects of IM on vocabulary knowledge; and while EM's direct effect over and above that of learning strategies did not achieve significance, its indirect effect was significant and positive.
Conclusions: The findings suggest that vocabulary-learning strategies mediate the relationship between motivation and vocabulary knowledge. In addition, IM may have a greater influence on vocabulary learning in foreign-language contexts.
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http://dx.doi.org/10.1111/bjep.12135 | DOI Listing |
Methods Inf Med
January 2025
Artificial Intelligence Lab, Mimos Berhad, Kuala Lumpur, Malaysia.
Objective: This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a Natural Language Processing system, that extracts clinical information for healthcare management. The model utilizes word embedding to ensure feasibility of use, not in real-time but for secondary analysis, within the constraints of low-resource settings.
View Article and Find Full Text PDFJ Speech Lang Hear Res
January 2025
Department of Speech, Language, and Hearing Sciences, The University of Arizona, Tucson.
Purpose: The purpose of this study was to determine if the Vocabulary Acquisition and Usage for Late Talkers (VAULT) intervention could be efficaciously applied to a new treatment target: words a child neither understood nor said. We also assessed whether the type of context variability used to encourage semantic learning (i.e.
View Article and Find Full Text PDFBr J Educ Psychol
January 2025
Department of employment and admission, Changsha University, Changsha, China.
Aim: From the perspective of cognitive load theory, the present study examined the relative effectiveness of the sequential use of L1 and bilingual subtitles on incidental English vocabulary learning.
Methods: A total of 162 upper-intermediate Chinese learners of English as a foreign language watched an English clip in one of 4 subtitling conditions: L1-bilingual, bilingual-bilingual, L2-L2, and no subtitles.
Results: Results suggested a statistically significant advantage for the L1-bilingual condition over other conditions for word form and meaning recall.
Appl Clin Inform
January 2025
Pediatrics, Ohio State University College of Medicine, Columbus, United States.
Objective: To review pediatric artificial intelligence (AI) implementation studies from 2010-2021 and analyze reported performance measures.
Methods: We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE and Web of Science with controlled vocabulary.
Inclusion Criteria: AI intervention in a pediatric clinical setting that learns from data (i.
Cureus
December 2024
Obstetrics and Gynecology, ESI Hospital and Postgraduate Institute of Medical Sciences and Research (PGIMER) Basaidarapur, New Delhi, IND.
Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!